5%, 80.5%, 74.7%, 72.9%, 80.0% and 74.1%.

The huge impact of the coronavirus disease-2019 pandemic on childhood vaccinations will require urgent vaccination recovery plans with innovative approaches and future action plans to maintain vaccination coverage during any subsequent pandemics.

The huge impact of the coronavirus disease-2019 pandemic on childhood vaccinations will require urgent vaccination recovery plans with innovative approaches and future action plans to maintain vaccination coverage during any subsequent pandemics.

To explore the changing patterns of long-stay patients (LSP) to improve the utilization of pediatric intensive care units (PICUs) resources.

This is a 2-points cross-sectional study (5 years apart; 2014-2019) conducted among PICUs and SCICUs in Riyadh, Saudi Arabia. Children who have stayed in PICU for more than 21 days were included.

Out of the 11 units approached, 10 (90%) agreed to participate. The prevalence of LSP in all these hospitals decreased from 32% (48/150) in 2014 to 23.4% (35/149) in 2019. The length of stay ranged from 22 days to 13.5 years. The majority of LSP had a neuromuscular or cardiac disease and were admitted with respiratory compromise. Ventilator-associated pneumonia was the most prevalent complication (37.5%). The most commonly used resources were mechanical ventilation (93.8%), antibiotics (60.4%), and blood-products transfusions (35.4%). The most common reason for the extended stay was medical reasons (51.1%), followed by a lack of family resources (26.5%) or lack of referral to long-term care facilities (22.4%).

A long-stay is associated with significant critical care bed occupancy, complications, and utilization of resources that could be otherwise utilized as surge capacity for critical care services. Decreasing occupancy in this multicenter study deserves further engagement of the healthcare leaders and families to maximize the utilization of resources.

A long-stay is associated with significant critical care bed occupancy, complications, and utilization of resources that could be otherwise utilized as surge capacity for critical care services. Decreasing occupancy in this multicenter study deserves further engagement of the healthcare leaders and families to maximize the utilization of resources.

To determine the factors associated with the development of methicillin-resistant Staphylococcus aureus (MRSA), hospital stay and mortality, and early versus late MRSA infection.

Cases (n=44) were intensive care unit (ICU) patients admitted to King Fahd Specialist Hospital, Al-Qassim, Saudi Arabia between 2015 and 2019 who developed MRSA during their hospital stay. Controls (n=48) were patients from the same place and period who did not develop MRSA. Data were abstracted from hospital records.

Admission with sepsis (case 46% vs. learn more control 2%, p less than 0.001) and having at least one comorbid condition (case 95% vs. control 46%, p less than 0.001) were significantly associated with the development of MRSA. Age (mean ±SD case 65±18, control 64±18, p=0.7) and gender (% male, case 52%, control 56%, p=0.70) were not associated with the development of MRSA. Approximately 73% of all MRSA cases developed within the first 2 weeks of admission. Among the early cases, 44% died during their ICU stay; the correspond32) or comorbid status (at least one 97% vs. 92%, p=0.17). Conclusion Sepsis and comorbid conditions were significant risk factors for MRSA development among hospital patients.Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.

To determine the association between comorbidities and the severity of the disease among COVID-19 patients.

We searched the Cochrane, Medline, Trip, and EMBASE databases from 2019. The review included all available studies of COVID-19 patients published in the English language and studied the clinical characteristics, comorbidities, and disease outcomes from the beginning of the pandemic. Two authors extracted studies characteristics and the risk of bias. Odds ratio (OR) was used to analyze the data with 95% confidence interval (CI).

The review included 1,885 COVID-19 patients from 7 observational studies with some degree of bias risk and substantial heterogeneity. A significant association was recorded between COVID-19 severity and the following variables male (OR= 1.60, 95%CI= 1.05 – 2.43); current smoker (OR=2.06, 95%CI= 1.08 – 3.94); and the presence of comorbidities including hypertension (OR=2.05, 95%CI= 1.56 – 2.70), diabetes (OR=2.46, 95%CI= 1.53 – 3.96), coronary heart disease (OR=4.10, 95%CI= 2.36 – 7.12), chronic kidney disease (OR=4.06, 95%CI= 1.45 – 11.35), and cancer (OR=2.28, 95%CI= 1.08 – 4.81).

Comorbidities among COVID-19 patients may contribute to increasing their susceptibility to severe illness. The identification of these potential risk factors could help reduce mortality by identifying patients with poor prognosis at an early stage.

Comorbidities among COVID-19 patients may contribute to increasing their susceptibility to severe illness. The identification of these potential risk factors could help reduce mortality by identifying patients with poor prognosis at an early stage.